{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "3759ab0a",
   "metadata": {},
   "outputs": [],
   "source": [
    "# 高中体测数据可视化（体测分数_男生，体测分数_女生）【数据见作业附件】\n",
    "\n",
    "# 1、对男1000米跑、男引体分数进行等宽分箱操作，分成3份，并使用饼图绘制百分比\n",
    "\n",
    "# 2、对女800米跑、女跳远进行直方图绘制统计各分数段人数，分成4份\n",
    "\n",
    "# 3、使用嵌套饼图对比男女生体重指数BMI进行比例统计，分为正常、低体重、超重、肥胖\n",
    "\n",
    "# BMI评分表:\n",
    "\n",
    "#       男生\t   女生\n",
    "# 低体重: \t   <=16.4\t     <=16.4\n",
    "#    正常: \t 16.5~23.2\t  16.5~22.7\n",
    "#    超重: \t 23.3~26.3\t  22.8~25.2\n",
    "#    肥胖: \t   >=26.4\t    >=25.3"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "25ad7933",
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import matplotlib.pyplot as plt # 画图的画笔\n",
    "import pandas as pd\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "dd2c9d6d",
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.read_excel(\"./data/体测数据/体测分数_男生.xls\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "0595ef4e",
   "metadata": {
    "collapsed": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>班级</th>\n",
       "      <th>性别</th>\n",
       "      <th>男1000米跑</th>\n",
       "      <th>男1000米跑分数</th>\n",
       "      <th>男50米跑</th>\n",
       "      <th>男50米跑分数</th>\n",
       "      <th>男跳远</th>\n",
       "      <th>男跳远分数</th>\n",
       "      <th>男体前屈</th>\n",
       "      <th>男体前屈分数</th>\n",
       "      <th>男引体</th>\n",
       "      <th>男引体分数</th>\n",
       "      <th>男肺活量</th>\n",
       "      <th>男肺活量分数</th>\n",
       "      <th>身高</th>\n",
       "      <th>体重</th>\n",
       "      <th>BMI</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>男</td>\n",
       "      <td>4.13</td>\n",
       "      <td>72</td>\n",
       "      <td>8.88</td>\n",
       "      <td>66</td>\n",
       "      <td>195</td>\n",
       "      <td>60</td>\n",
       "      <td>12</td>\n",
       "      <td>74</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>2785</td>\n",
       "      <td>62</td>\n",
       "      <td>170</td>\n",
       "      <td>72.599998</td>\n",
       "      <td>25.120001</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>男</td>\n",
       "      <td>4.16</td>\n",
       "      <td>70</td>\n",
       "      <td>7.70</td>\n",
       "      <td>78</td>\n",
       "      <td>225</td>\n",
       "      <td>74</td>\n",
       "      <td>11</td>\n",
       "      <td>74</td>\n",
       "      <td>7</td>\n",
       "      <td>60</td>\n",
       "      <td>3133</td>\n",
       "      <td>68</td>\n",
       "      <td>174</td>\n",
       "      <td>52.700001</td>\n",
       "      <td>17.410000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>男</td>\n",
       "      <td>4.09</td>\n",
       "      <td>74</td>\n",
       "      <td>8.45</td>\n",
       "      <td>70</td>\n",
       "      <td>218</td>\n",
       "      <td>70</td>\n",
       "      <td>14</td>\n",
       "      <td>78</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>3901</td>\n",
       "      <td>80</td>\n",
       "      <td>169</td>\n",
       "      <td>46.500000</td>\n",
       "      <td>16.280001</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>男</td>\n",
       "      <td>4.21</td>\n",
       "      <td>68</td>\n",
       "      <td>8.05</td>\n",
       "      <td>74</td>\n",
       "      <td>206</td>\n",
       "      <td>64</td>\n",
       "      <td>13</td>\n",
       "      <td>76</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>4946</td>\n",
       "      <td>100</td>\n",
       "      <td>183</td>\n",
       "      <td>79.699997</td>\n",
       "      <td>23.799999</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1</td>\n",
       "      <td>男</td>\n",
       "      <td>3.44</td>\n",
       "      <td>85</td>\n",
       "      <td>7.52</td>\n",
       "      <td>78</td>\n",
       "      <td>210</td>\n",
       "      <td>66</td>\n",
       "      <td>13</td>\n",
       "      <td>76</td>\n",
       "      <td>9</td>\n",
       "      <td>68</td>\n",
       "      <td>3538</td>\n",
       "      <td>74</td>\n",
       "      <td>171</td>\n",
       "      <td>54.700001</td>\n",
       "      <td>18.709999</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>472</th>\n",
       "      <td>17</td>\n",
       "      <td>男</td>\n",
       "      <td>4.23</td>\n",
       "      <td>68</td>\n",
       "      <td>8.27</td>\n",
       "      <td>72</td>\n",
       "      <td>208</td>\n",
       "      <td>66</td>\n",
       "      <td>10</td>\n",
       "      <td>72</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>4647</td>\n",
       "      <td>100</td>\n",
       "      <td>176</td>\n",
       "      <td>69.500000</td>\n",
       "      <td>22.440001</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>473</th>\n",
       "      <td>17</td>\n",
       "      <td>男</td>\n",
       "      <td>5.19</td>\n",
       "      <td>40</td>\n",
       "      <td>9.55</td>\n",
       "      <td>50</td>\n",
       "      <td>210</td>\n",
       "      <td>66</td>\n",
       "      <td>15</td>\n",
       "      <td>80</td>\n",
       "      <td>6</td>\n",
       "      <td>50</td>\n",
       "      <td>7042</td>\n",
       "      <td>100</td>\n",
       "      <td>177</td>\n",
       "      <td>76.000000</td>\n",
       "      <td>24.260000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>474</th>\n",
       "      <td>17</td>\n",
       "      <td>男</td>\n",
       "      <td>3.25</td>\n",
       "      <td>100</td>\n",
       "      <td>7.50</td>\n",
       "      <td>80</td>\n",
       "      <td>252</td>\n",
       "      <td>90</td>\n",
       "      <td>13</td>\n",
       "      <td>76</td>\n",
       "      <td>13</td>\n",
       "      <td>85</td>\n",
       "      <td>5755</td>\n",
       "      <td>100</td>\n",
       "      <td>181</td>\n",
       "      <td>65.000000</td>\n",
       "      <td>19.840000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>475</th>\n",
       "      <td>17</td>\n",
       "      <td>男</td>\n",
       "      <td>4.39</td>\n",
       "      <td>62</td>\n",
       "      <td>7.81</td>\n",
       "      <td>76</td>\n",
       "      <td>208</td>\n",
       "      <td>66</td>\n",
       "      <td>14</td>\n",
       "      <td>78</td>\n",
       "      <td>11</td>\n",
       "      <td>76</td>\n",
       "      <td>5688</td>\n",
       "      <td>100</td>\n",
       "      <td>172</td>\n",
       "      <td>51.700001</td>\n",
       "      <td>17.480000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>476</th>\n",
       "      <td>17</td>\n",
       "      <td>男</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>477 rows × 17 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     班级 性别  男1000米跑  男1000米跑分数  男50米跑  男50米跑分数  男跳远  男跳远分数  男体前屈  男体前屈分数  男引体  \\\n",
       "0     1  男     4.13         72   8.88       66  195     60    12      74    1   \n",
       "1     1  男     4.16         70   7.70       78  225     74    11      74    7   \n",
       "2     1  男     4.09         74   8.45       70  218     70    14      78    1   \n",
       "3     1  男     4.21         68   8.05       74  206     64    13      76    1   \n",
       "4     1  男     3.44         85   7.52       78  210     66    13      76    9   \n",
       "..   .. ..      ...        ...    ...      ...  ...    ...   ...     ...  ...   \n",
       "472  17  男     4.23         68   8.27       72  208     66    10      72    0   \n",
       "473  17  男     5.19         40   9.55       50  210     66    15      80    6   \n",
       "474  17  男     3.25        100   7.50       80  252     90    13      76   13   \n",
       "475  17  男     4.39         62   7.81       76  208     66    14      78   11   \n",
       "476  17  男     0.00          0   0.00        0    0      0     0       0    0   \n",
       "\n",
       "     男引体分数  男肺活量  男肺活量分数   身高         体重        BMI  \n",
       "0        0  2785      62  170  72.599998  25.120001  \n",
       "1       60  3133      68  174  52.700001  17.410000  \n",
       "2        0  3901      80  169  46.500000  16.280001  \n",
       "3        0  4946     100  183  79.699997  23.799999  \n",
       "4       68  3538      74  171  54.700001  18.709999  \n",
       "..     ...   ...     ...  ...        ...        ...  \n",
       "472      0  4647     100  176  69.500000  22.440001  \n",
       "473     50  7042     100  177  76.000000  24.260000  \n",
       "474     85  5755     100  181  65.000000  19.840000  \n",
       "475     76  5688     100  172  51.700001  17.480000  \n",
       "476      0     0       0    0   0.000000   0.000000  \n",
       "\n",
       "[477 rows x 17 columns]"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "ec734cb3",
   "metadata": {
    "collapsed": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(-0.101, 33.333]     29\n",
       "(33.333, 66.667]     68\n",
       "(66.667, 100.0]     380\n",
       "Name: 男1000米跑分数, dtype: int64"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "score1=df['男1000米跑分数'].value_counts(bins=3,sort=False)\n",
    "score1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "8d7fa7da",
   "metadata": {
    "collapsed": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(-0.101, 33.333]    121\n",
       "(33.333, 66.667]    137\n",
       "(66.667, 100.0]     219\n",
       "Name: 男引体分数, dtype: int64"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "score2=df['男引体分数'].value_counts(bins=3,sort=False)\n",
    "score2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "3a7d578d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'Arial',\n",
       " 'Arial Unicode MS',\n",
       " 'Bahnschrift',\n",
       " 'Calibri',\n",
       " 'Cambria',\n",
       " 'Candara',\n",
       " 'Century',\n",
       " 'Comic Sans MS',\n",
       " 'Consolas',\n",
       " 'Constantia',\n",
       " 'Corbel',\n",
       " 'Courier New',\n",
       " 'DFLiJinHeiW8-GB',\n",
       " 'DejaVu Sans',\n",
       " 'DejaVu Sans Display',\n",
       " 'DejaVu Sans Mono',\n",
       " 'DejaVu Serif',\n",
       " 'DejaVu Serif Display',\n",
       " 'DengXian',\n",
       " 'Ebrima',\n",
       " 'FZShuTi',\n",
       " 'FZYaoTi',\n",
       " 'FZZJ-XSS',\n",
       " 'FangSong',\n",
       " 'FlemishScript BT',\n",
       " 'Franklin Gothic Medium',\n",
       " 'Gabriola',\n",
       " 'Gadugi',\n",
       " 'Georgia',\n",
       " 'HP Simplified',\n",
       " 'HoloLens MDL2 Assets',\n",
       " 'Impact',\n",
       " 'Ink Free',\n",
       " 'Javanese Text',\n",
       " 'KaiTi',\n",
       " 'Leelawadee',\n",
       " 'Leelawadee UI',\n",
       " 'LiSu',\n",
       " 'Lucida Console',\n",
       " 'Lucida Sans Unicode',\n",
       " 'MS Gothic',\n",
       " 'MT Extra',\n",
       " 'MV Boli',\n",
       " 'Malgun Gothic',\n",
       " 'Marlett',\n",
       " 'Microsoft Himalaya',\n",
       " 'Microsoft JhengHei',\n",
       " 'Microsoft New Tai Lue',\n",
       " 'Microsoft PhagsPa',\n",
       " 'Microsoft Sans Serif',\n",
       " 'Microsoft Tai Le',\n",
       " 'Microsoft Uighur',\n",
       " 'Microsoft YaHei',\n",
       " 'Microsoft Yi Baiti',\n",
       " 'MingLiU-ExtB',\n",
       " 'Mongolian Baiti',\n",
       " 'Myanmar Text',\n",
       " 'Nirmala UI',\n",
       " 'Palatino Linotype',\n",
       " 'STCaiyun',\n",
       " 'STFangsong',\n",
       " 'STHupo',\n",
       " 'STIXGeneral',\n",
       " 'STIXNonUnicode',\n",
       " 'STIXSizeFiveSym',\n",
       " 'STIXSizeFourSym',\n",
       " 'STIXSizeOneSym',\n",
       " 'STIXSizeThreeSym',\n",
       " 'STIXSizeTwoSym',\n",
       " 'STKaiti',\n",
       " 'STLiti',\n",
       " 'STSong',\n",
       " 'STXihei',\n",
       " 'STXingkai',\n",
       " 'STXinwei',\n",
       " 'STZhongsong',\n",
       " 'Segoe MDL2 Assets',\n",
       " 'Segoe Print',\n",
       " 'Segoe Script',\n",
       " 'Segoe UI',\n",
       " 'Segoe UI Emoji',\n",
       " 'Segoe UI Historic',\n",
       " 'Segoe UI Symbol',\n",
       " 'SimHei',\n",
       " 'SimSun',\n",
       " 'SimSun-ExtB',\n",
       " 'Sitka Small',\n",
       " 'Sylfaen',\n",
       " 'Symbol',\n",
       " 'Tahoma',\n",
       " 'Times New Roman',\n",
       " 'Trebuchet MS',\n",
       " 'Verdana',\n",
       " 'Webdings',\n",
       " 'Wingdings',\n",
       " 'Wingdings 2',\n",
       " 'Wingdings 3',\n",
       " 'YouYuan',\n",
       " 'Yu Gothic',\n",
       " 'cmb10',\n",
       " 'cmex10',\n",
       " 'cmmi10',\n",
       " 'cmr10',\n",
       " 'cmss10',\n",
       " 'cmsy10',\n",
       " 'cmtt10'}"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from matplotlib.font_manager import FontManager\n",
    "fm = FontManager()\n",
    "mat_fonts = set(f.name for f in fm.ttflist)\n",
    "mat_fonts"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "id": "043cc7aa",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 400x400 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib\n",
    "# 解决中⽂字体乱码的问题\n",
    "matplotlib.rcParams['font.sans-serif']='STXinwei'\n",
    "labels =[\"差\",\"中\",\"好\"] # 标签\n",
    "# percent = [95,261,105,30,9]\n",
    "# 设置图⽚⼤⼩和分辨率\n",
    "fig=plt.figure(figsize=(4,4), dpi=100)\n",
    "# 偏移中⼼量，突出某⼀部分\n",
    "explode = (0, 0.1, 0)\n",
    "# 绘制饼图： autopct显示百分⽐，这⾥保留⼀位⼩数； shadow控制是否显示阴影\n",
    "_=plt.pie(x = score1, # 数据\n",
    "explode=explode, # 偏移中⼼量\n",
    "labels=labels, # 显示标签\n",
    "autopct='%0.1f%%', # 显示百分⽐\n",
    "shadow=True) # 阴影， 3D效果\n",
    "#title\n",
    "plt.title('男1000米跑分数分布')\n",
    "plt.savefig(\"./男1000米跑分数分布饼图.jpg\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "id": "230765b4",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 400x400 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig=plt.figure(figsize=(4,4), dpi=100)\n",
    "# 绘制饼图： autopct显示百分⽐，这⾥保留⼀位⼩数； shadow控制是否显示阴影\n",
    "_=plt.pie(x = score2, # 数据\n",
    "explode=explode, # 偏移中⼼量\n",
    "labels=labels, # 显示标签\n",
    "autopct='%0.1f%%', # 显示百分⽐\n",
    "shadow=True) # 阴影， 3D效果\n",
    "\n",
    "\n",
    "plt.title('男引体分数分布')\n",
    "plt.savefig(\"./男引体分数分布饼图.jpg\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "id": "a3df3d04",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>班级</th>\n",
       "      <th>性别</th>\n",
       "      <th>女800米跑</th>\n",
       "      <th>女800米跑分数</th>\n",
       "      <th>女50米跑</th>\n",
       "      <th>女50米跑分数</th>\n",
       "      <th>女跳远</th>\n",
       "      <th>女跳远分数</th>\n",
       "      <th>女体前屈</th>\n",
       "      <th>女体前屈分数</th>\n",
       "      <th>女仰卧</th>\n",
       "      <th>女仰卧分数</th>\n",
       "      <th>女肺活量</th>\n",
       "      <th>女肺活量分数</th>\n",
       "      <th>身高</th>\n",
       "      <th>体重</th>\n",
       "      <th>BMI</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>女</td>\n",
       "      <td>3.22</td>\n",
       "      <td>100</td>\n",
       "      <td>9.32</td>\n",
       "      <td>72</td>\n",
       "      <td>185</td>\n",
       "      <td>85</td>\n",
       "      <td>16</td>\n",
       "      <td>76</td>\n",
       "      <td>48</td>\n",
       "      <td>85</td>\n",
       "      <td>3775</td>\n",
       "      <td>100</td>\n",
       "      <td>163.0</td>\n",
       "      <td>51.299999</td>\n",
       "      <td>19.309999</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>女</td>\n",
       "      <td>4.59</td>\n",
       "      <td>40</td>\n",
       "      <td>11.44</td>\n",
       "      <td>10</td>\n",
       "      <td>148</td>\n",
       "      <td>60</td>\n",
       "      <td>9</td>\n",
       "      <td>66</td>\n",
       "      <td>29</td>\n",
       "      <td>66</td>\n",
       "      <td>3683</td>\n",
       "      <td>100</td>\n",
       "      <td>163.0</td>\n",
       "      <td>66.599998</td>\n",
       "      <td>25.070000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>女</td>\n",
       "      <td>3.46</td>\n",
       "      <td>80</td>\n",
       "      <td>13.40</td>\n",
       "      <td>0</td>\n",
       "      <td>150</td>\n",
       "      <td>60</td>\n",
       "      <td>7</td>\n",
       "      <td>64</td>\n",
       "      <td>40</td>\n",
       "      <td>76</td>\n",
       "      <td>3331</td>\n",
       "      <td>100</td>\n",
       "      <td>157.0</td>\n",
       "      <td>60.000000</td>\n",
       "      <td>24.340000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>女</td>\n",
       "      <td>3.39</td>\n",
       "      <td>85</td>\n",
       "      <td>9.52</td>\n",
       "      <td>70</td>\n",
       "      <td>172</td>\n",
       "      <td>76</td>\n",
       "      <td>21</td>\n",
       "      <td>90</td>\n",
       "      <td>46</td>\n",
       "      <td>85</td>\n",
       "      <td>3701</td>\n",
       "      <td>100</td>\n",
       "      <td>160.0</td>\n",
       "      <td>50.700001</td>\n",
       "      <td>19.799999</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1</td>\n",
       "      <td>女</td>\n",
       "      <td>3.43</td>\n",
       "      <td>80</td>\n",
       "      <td>9.79</td>\n",
       "      <td>68</td>\n",
       "      <td>145</td>\n",
       "      <td>50</td>\n",
       "      <td>8</td>\n",
       "      <td>64</td>\n",
       "      <td>34</td>\n",
       "      <td>70</td>\n",
       "      <td>3592</td>\n",
       "      <td>100</td>\n",
       "      <td>167.0</td>\n",
       "      <td>63.900002</td>\n",
       "      <td>22.910000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>588</th>\n",
       "      <td>17</td>\n",
       "      <td>女</td>\n",
       "      <td>3.51</td>\n",
       "      <td>78</td>\n",
       "      <td>9.60</td>\n",
       "      <td>68</td>\n",
       "      <td>150</td>\n",
       "      <td>60</td>\n",
       "      <td>24</td>\n",
       "      <td>95</td>\n",
       "      <td>41</td>\n",
       "      <td>78</td>\n",
       "      <td>2255</td>\n",
       "      <td>70</td>\n",
       "      <td>158.0</td>\n",
       "      <td>49.000000</td>\n",
       "      <td>19.629999</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>589</th>\n",
       "      <td>17</td>\n",
       "      <td>女</td>\n",
       "      <td>4.00</td>\n",
       "      <td>76</td>\n",
       "      <td>10.18</td>\n",
       "      <td>64</td>\n",
       "      <td>150</td>\n",
       "      <td>60</td>\n",
       "      <td>13</td>\n",
       "      <td>72</td>\n",
       "      <td>36</td>\n",
       "      <td>72</td>\n",
       "      <td>2937</td>\n",
       "      <td>85</td>\n",
       "      <td>161.0</td>\n",
       "      <td>55.700001</td>\n",
       "      <td>21.490000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>590</th>\n",
       "      <td>17</td>\n",
       "      <td>女</td>\n",
       "      <td>3.45</td>\n",
       "      <td>80</td>\n",
       "      <td>10.18</td>\n",
       "      <td>64</td>\n",
       "      <td>152</td>\n",
       "      <td>62</td>\n",
       "      <td>15</td>\n",
       "      <td>76</td>\n",
       "      <td>35</td>\n",
       "      <td>72</td>\n",
       "      <td>2592</td>\n",
       "      <td>76</td>\n",
       "      <td>165.0</td>\n",
       "      <td>48.599998</td>\n",
       "      <td>17.850000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>591</th>\n",
       "      <td>17</td>\n",
       "      <td>女</td>\n",
       "      <td>4.01</td>\n",
       "      <td>74</td>\n",
       "      <td>9.67</td>\n",
       "      <td>68</td>\n",
       "      <td>165</td>\n",
       "      <td>70</td>\n",
       "      <td>10</td>\n",
       "      <td>68</td>\n",
       "      <td>41</td>\n",
       "      <td>78</td>\n",
       "      <td>1829</td>\n",
       "      <td>60</td>\n",
       "      <td>154.0</td>\n",
       "      <td>43.599998</td>\n",
       "      <td>18.379999</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>592</th>\n",
       "      <td>17</td>\n",
       "      <td>女</td>\n",
       "      <td>4.48</td>\n",
       "      <td>50</td>\n",
       "      <td>9.09</td>\n",
       "      <td>74</td>\n",
       "      <td>180</td>\n",
       "      <td>80</td>\n",
       "      <td>10</td>\n",
       "      <td>68</td>\n",
       "      <td>46</td>\n",
       "      <td>85</td>\n",
       "      <td>2962</td>\n",
       "      <td>85</td>\n",
       "      <td>162.0</td>\n",
       "      <td>55.299999</td>\n",
       "      <td>21.070000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>593 rows × 17 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     班级 性别  女800米跑  女800米跑分数  女50米跑  女50米跑分数  女跳远  女跳远分数  女体前屈  女体前屈分数  女仰卧  \\\n",
       "0     1  女    3.22       100   9.32       72  185     85    16      76   48   \n",
       "1     1  女    4.59        40  11.44       10  148     60     9      66   29   \n",
       "2     1  女    3.46        80  13.40        0  150     60     7      64   40   \n",
       "3     1  女    3.39        85   9.52       70  172     76    21      90   46   \n",
       "4     1  女    3.43        80   9.79       68  145     50     8      64   34   \n",
       "..   .. ..     ...       ...    ...      ...  ...    ...   ...     ...  ...   \n",
       "588  17  女    3.51        78   9.60       68  150     60    24      95   41   \n",
       "589  17  女    4.00        76  10.18       64  150     60    13      72   36   \n",
       "590  17  女    3.45        80  10.18       64  152     62    15      76   35   \n",
       "591  17  女    4.01        74   9.67       68  165     70    10      68   41   \n",
       "592  17  女    4.48        50   9.09       74  180     80    10      68   46   \n",
       "\n",
       "     女仰卧分数  女肺活量  女肺活量分数     身高         体重        BMI  \n",
       "0       85  3775     100  163.0  51.299999  19.309999  \n",
       "1       66  3683     100  163.0  66.599998  25.070000  \n",
       "2       76  3331     100  157.0  60.000000  24.340000  \n",
       "3       85  3701     100  160.0  50.700001  19.799999  \n",
       "4       70  3592     100  167.0  63.900002  22.910000  \n",
       "..     ...   ...     ...    ...        ...        ...  \n",
       "588     78  2255      70  158.0  49.000000  19.629999  \n",
       "589     72  2937      85  161.0  55.700001  21.490000  \n",
       "590     72  2592      76  165.0  48.599998  17.850000  \n",
       "591     78  1829      60  154.0  43.599998  18.379999  \n",
       "592     85  2962      85  162.0  55.299999  21.070000  \n",
       "\n",
       "[593 rows x 17 columns]"
      ]
     },
     "execution_count": 90,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "\n",
    "dff = pd.read_excel(\"./data/体测数据/体测分数_女生.xls\")\n",
    "dff"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 101,
   "id": "f8dd15b2",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 2、对女800米跑、女跳远进行直方图绘制统计各分数段人数，分成4份\n",
    "\n",
    "\n",
    "mu = dff2['女跳远'].mean().round(1)\n",
    "\n",
    "sigma=dff2['女跳远'].std().round(1)\n",
    "\n",
    "    \n",
    "matplotlib.rcParams['font.sans-serif']='STXinwei'\n",
    "fig, ax = plt.subplots()\n",
    "n, bins, patches = ax.hist(dff2['女跳远'], 4, density=True) # 直⽅图\n",
    "\n",
    "# 概率密度函数\n",
    "y = ((1 / (np.sqrt(2 * np.pi) * sigma)) *\n",
    "np.exp(-0.5 * (1 / sigma * (bins - mu))**2))\n",
    "plt.plot(bins, y, '--')\n",
    "plt.xlabel('Smarts')\n",
    "plt.ylabel('Probability density')\n",
    "plt.title(r'Histogram of 女跳远')\n",
    "# 紧凑布局\n",
    "fig.tight_layout()\n",
    "plt.savefig('./女跳远直⽅图.png')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 102,
   "id": "580ee233",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 2、对女800米跑、女跳远进行直方图绘制统计各分数段人数，分成4份\n",
    "#去除异常值\n",
    "con = df_f['女800米跑']<6.0\n",
    "dff2 = df_f[con]\n",
    "\n",
    "mu = dff2['女800米跑'].mean().round(1)\n",
    "\n",
    "sigma=dff2['女800米跑'].std().round(1)\n",
    "\n",
    "    \n",
    "matplotlib.rcParams['font.sans-serif']='STXinwei'\n",
    "fig, ax = plt.subplots()\n",
    "n, bins, patches = ax.hist(dff2['女800米跑'], 4, density=True) # 直⽅图\n",
    "\n",
    "# 概率密度函数\n",
    "y = ((1 / (np.sqrt(2 * np.pi) * sigma)) *\n",
    "np.exp(-0.5 * (1 / sigma * (bins - mu))**2))\n",
    "plt.plot(bins, y, '--')\n",
    "plt.xlabel('Smarts')\n",
    "plt.ylabel('Probability density')\n",
    "plt.title(r'Histogram of 女800米跑')\n",
    "# 紧凑布局\n",
    "fig.tight_layout()\n",
    "plt.savefig('./女800米跑直⽅图.png')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 103,
   "id": "f23b8635",
   "metadata": {},
   "outputs": [],
   "source": [
    "# 3、使用嵌套饼图对比男女生体重指数BMI进行比例统计，分为正常、低体重、超重、肥胖\n",
    "\n",
    "# BMI评分表:\n",
    "\n",
    "#       男生\t   女生\n",
    "# 低体重: \t   <=16.4\t     <=16.4\n",
    "#    正常: \t 16.5~23.2\t  16.5~22.7\n",
    "#    超重: \t 23.3~26.3\t  22.8~25.2\n",
    "#    肥胖: \t   >=26.4\t    >=25.3"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 108,
   "id": "fdad90c6",
   "metadata": {},
   "outputs": [],
   "source": [
    "def convert(x):\n",
    "    if x <=16.4:\n",
    "        return '低体重'\n",
    "    elif x >16.4 and  x <=23.2 :\n",
    "        return '正常'\n",
    "    elif  x >23.2 and x<=26.3:\n",
    "        return '超重'\n",
    "    elif x >26.3:\n",
    "        return '肥胖'\n",
    "x1=df['BMI'].map(convert).value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 109,
   "id": "849f1168",
   "metadata": {},
   "outputs": [],
   "source": [
    "def convertf(x):\n",
    "    if x <=16.4:\n",
    "        return '低体重'\n",
    "    elif x >16.4 and  x <=22.7 :\n",
    "        return '正常'\n",
    "    elif  x >22.7 and x<=25.2:\n",
    "        return '超重'\n",
    "    elif x >25.2:\n",
    "        return '肥胖'\n",
    "x2 = dff['BMI'].map(convertf).value_counts"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 115,
   "id": "48b27107",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "d:\\anaconda3\\envs\\tensorflow_env\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:238: RuntimeWarning: Glyph 12112 missing from current font.\n",
      "  font.set_text(s, 0.0, flags=flags)\n",
      "d:\\anaconda3\\envs\\tensorflow_env\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:201: RuntimeWarning: Glyph 12112 missing from current font.\n",
      "  font.set_text(s, 0, flags=flags)\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 500x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "matplotlib.rcParams['font.sans-serif']='STXinwei'\n",
    "fig=plt.figure(figsize=(5,5),dpi=100)\n",
    "\n",
    "labels = ['肥胖','超重','正常','低体重']\n",
    "def func(pct):\n",
    "   return r'%0.1f'%(pct) + '%'\n",
    "\n",
    "_=plt.pie(x1,\n",
    "    autopct=lambda pct: func(pct),\n",
    "    radius=1, # 半径\n",
    "    pctdistance=0.85, # 百分⽐位置\n",
    "    wedgeprops=dict(linewidth=3,width=0.4,edgecolor='w'),# 饼图格式：间隔线宽、饼图宽度、边界颜⾊\n",
    "    labels=labels)\n",
    "# 绘制内部饼图\n",
    "_=plt.pie(x2,autopct='%0.1f%%',\n",
    "        radius=0.7,\n",
    "        pctdistance=0.7,\n",
    "        wedgeprops=dict(linewidth=3,width=0.7,edgecolor='w'))\n",
    "        # 设置图例标题、位置， frameon控制是否显示图例边框， bbox_to_anchor控制图例显示在饼图的外⾯\n",
    "_=plt.legend(labels,loc = 'upper right',bbox_to_anchor = (0.75,0,0.4,1),title =\n",
    "        'IBM占⽐')\n",
    "\n",
    "\n",
    "plt.savefig('./男女IBM占⽐.png')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "a9b06156",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.2"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}
